Sound Processing Device Ego-Noise Removal Adaptation
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Solution Overview
Problem
Existing sound processing devices face challenges in effectively reducing ego-noise generated by mechanical apparatuses, such as robots, due to the need for numerous templates to handle varying noise conditions, which increases processing time and reduces noise-suppressing performance.
Innovation Solution
A sound processing device that includes a storage unit for operation data and sound feature values, a noise estimating unit to calculate noise components, and an updating unit to adapt sound feature values based on detected operation data and noise estimates, allowing for improved noise removal performance and adaptability to varying noise characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a limited number of templates are used for noise removal, then processing time is reduced, but noise-suppressing performance deteriorates under varying noise conditions
Solution Approach 1:
The patent implements dynamic template generation by creating noise removal templates in real-time based on the current noise characteristics detected in the input signal, rather than using a fixed limited set of pre-prepared templates. This allows the system to adapt to varying noise conditions dynamically while maintaining efficient processing.
Solution Approach 2:
The system performs self-learning by automatically analyzing the noise components in the input signal and generating appropriate removal templates without requiring external preparation or selection from a large template database. The noise removal template is created on-demand based on the actual noise present in the signal.
2Reliability
If many templates are prepared to handle various noise conditions, then noise-suppressing performance is improved, but processing time increases
Solution Approach 1:
The system dynamically generates noise removal templates based on the actual noise characteristics in the input signal, eliminating the need to store and search through numerous pre-prepared templates. This dynamic approach ensures high noise suppression performance for the current noise condition without the time penalty of searching a large template database.
Solution Approach 2:
The patent creates a noise removal template that is specific to the current noise instance by analyzing the noise components in the input signal. This copied/adapted template is then applied to remove the specific noise present, providing high effectiveness without requiring a comprehensive library of all possible noise templates.
3Adaptability or versatility
If templates are updated frequently to adapt to changing noise conditions, then adaptability is improved, but system stability deteriorates
Solution Approach 1:
The system implements dynamic template generation that adapts to changing noise conditions in real-time by analyzing the current noise characteristics and generating appropriate removal templates. This dynamic approach provides high adaptability while maintaining stability through consistent application of the generated templates to their corresponding noise instances.
Solution Approach 2:
The patent employs a feedback mechanism where the noise characteristics in the input signal are analyzed, and this information feeds back into the template generation process. The generated template is then applied and its effectiveness is implicitly evaluated, creating a closed-loop system that adapts to noise changes while maintaining operational stability.
Data Source
AI summary
A sound processing device includes a storage unit configured to store first operation data corresponding to a motion of a mechanical apparatus and a first sound feature value corresponding to the motion in correlation with each other, a noise estimating unit configured to estimate a third sound feature value corresponding to a noise component based on a second sound feature value corresponding to an acquired sound signal, a sound feature value processing unit configured to calculate a target sound feature value from which the noise component is removed based on the second sound feature value and the third sound feature value, and an updating unit that updates the first sound feature value stored in the storage unit based on detected second operation data and the third sound feature value estimated by the noise estimating unit.